However, as a Product Manager, how do you gain the necessary knowledge to analyze, understand, plan, and design products based on Artificial Intelligence technologies? Since you cannot get a college degree in AI Product Management, how do you adapt to this rapid change? Adnan Boz, former PM at Yahoo, will be answering all your doubts.

Why Is AI Important?

One of the primary reasons for companies to integrate AI is the presence of too much competition in the world.

If the CEO of a company has no vision/strategy for AI, then the company is surely going down.

The United States specifically needs to invest in AI because they will never have a chance of the lowest cost of labour or raw materials.

How to Build an AI Strategy for your Business or Product?

In order to keep pace with the growing competition in AI, it is first important to change your strategy. A strategy is an organizational response to environmental change, which is AI in this case. Building an AI strategy for your business involves cognification of every business aspect.

Below are the 5 business aspects of an organization with strategies on how to integrate AI with each one of them:

1- People

The foremost important task is to identify an AI Product Manager. They need to know the AI product lifecycle and have specific AI solution understanding. An AI PM also needs to possess core management skills and be an industry expert in their respective domains.

There is a need for a different strategy due to various AI shortcomings like lack of AI higher education degree, no standardized AI product methodologies, and almost no progress in AI vision and strategy building.

The Strategy

Hiring an AI Product Manager is essential for the cognification of your business and product.

If you don’t find a suitable AI PM, you can get the existing Data Scientists or AI Architects in your team to take courses/training on Deep Learning or other ML algorithms. Or post your vacancy on this Product job portal and reach the world’s largest PM community.

Another option is to consult or outsource your work to expert companies like Move to AI, NVIDIA, etc, for cognification of your business.

You can also use existing tools like DataRobot to cognify your business which does a Data Scientist’s job in one week.

3- Practices

The first step is to identify the AI opportunities and build around the process that fits our needs as well as help in cognification.

For example, if your company is looking at automating or optimizing certain processes, existing ML algorithms can be used whereas if you’re looking at expanding your business to different geographical locations, there is a need for disruptive innovation technologies.

There are different strategies to deal with different kinds of processes as listed below.

The Strategy

Since AI is about searching for the best and most efficient algorithms, using a flexible framework, such as Agile AI, will benefit cognification of the practices.

Automation – For automating processes, the right methods to use are Agile and GV Design Sprints.

Optimization – For optimizing processes, the right methods to use are Lean Startup and GV Design Sprint.

Expansion – For expanding your business, the right methods to use areDesign Thinking and Agile.

Innovation – For disruptive innovation, the right methods to use are Design Thinking and Lean Startup.

You can even combine multiple methods for cognification of a single practice.

3- Solutions

Finding the right AI solutions depends on the kind of problem you’re trying to solve. For example, if you want to integrate AI with an A/C and want to control the temperature based on Date, Weather, Preferences and Temperature, the most suitable model would be to use a Neural Network.

The Strategy

If the problems include Prediction, Fraud Detection, Image Classification, and Sentiment Analysis, use supervised machine learning algorithms which model inputs to known outputs of data.

If the problems include building Recommendation Systems, Compression, Targeting, etc., use unsupervised machine learning algorithms which model unknown patterns in the data.

If the problems include Robot Navigation, Gaming AI, Dynamic Pricing, and deciding the Next Best Offer, use reinforcement learning which models policies relative to the environment.

4- Environment

One of the worst hidden dangers of AI is having a decision-making system that is biased, also called as Black Box AI. One can use the below strategies to overcome this problem.

The Strategy

You can use a tool called LIME (Local Interpretable Model-Agnostic Explanations) which helps you gain transparency by allowing you to check why the model made a particular decision and what are the inputs/outputs it considered.

Have a risk management strategy or framework in place.

The AI PM’s responsibility is to adapt to a safety mindset by using the Artificial General Intelligence (AGI) strategy.

With hackers trying to break systems every now and then, AI is no exception. There are hackers who can modify few pixels of an image and get the ML model to predict wrong results. Therefore, infusing security during cognification is extremely crucial.

5- Relationships

When working on cognification of your business, it is important to identify partnerships that can help your business.

The Strategy

If you’re a fairly mid-sized company, you might have to refer to IPs and papers published by other companies and in this case, having an effective partnership with that company can help.

If you’re looking at scaling your business or entering international markets, you can work with companies who will take your product and apply it to other geo locations.

For licensing and regulations of your products, you can partner with existing companies and there is no need to create a separate team for it.

Lastly, train your Executives to have an AI mindset.

This article is based on Adnan Boz’ talk “AI for Product Managers

Meet Adnan Boz

Adnan Boz is the co-founder of Move to AI consulting and former Yahoo! Product Manager. Currently, he is working as a Lead Product Manager at eBay. In the past two decades and lately during his work at Yahoo! Homepage, Sports, Finance news, and 20+ international news feed, he developed a unique mix of technologies. He has also developed product know-how around news personalization, Machine Learning, online advertisement, analytics and user-news-interaction at the scale of billions of users.